How do you develop a data strategy that moves beyond planning? This article uses the flywheel principle to show how concrete use cases, clear accountability, and visible business value can build lasting momentum.
AI use cases can be collected quickly, but prioritizing them is what determines their actual value. Learn how to cluster ideas effectively, build strategic capabilities, and establish a sound starting point with a Proof of Value.
A data roadmap does not have to be a rigid five-year plan. Learn how to structure your data journey across four sequential horizons, make value visible early, and keep business, people, and technology moving in sync.
How can small improvements in revenue, costs, and workflows have a disproportionately large impact on margins? Using a simple calculation, this article shows how data, AI, and automation can help you boost productivity in a targeted way.
What do Claude Monet and the quality of your company’s data have in common? This article explains why blurry, inconsistent data can distort decisions—and how to create a reliable foundation for management, analytics, and AI.
The saying that "data is the gold of the 21st century" has been encountered by almost everyone in recent years. But there is a crucial difference between data and gold. Data only have value when you use them. In order to enable usage, something has to change in many cases – the data have to be democratized.
In an analysis of the causes of the financial crisis of 2008, the European Central Bank (ECB) and many other regulatory authorities discovered, inter alia, major deficiencies in institutional IT and data architecture. As a result of these deficiencies, risk assessments could not be performed sufficiently quickly and accurately, and not completely either. In short: Risks could neither be reported nor controlled to an appropriate extent. In the wake of this lesson, the Basel Committee on Banking Supervision (BCBS) published standard 239, widely known through press releases and also referred to as the "principles for effective risk data aggregation and risk reporting" and AT 4.3.4 of MaRisk.
Just as most of us prefer harmonic chords to dissonance in music, value creation is maximized only by harmonizing data strategy, data organization and data governance. We will address the most important issues and provide successful examples.
Currently, a popular component of Cloud Data Platform Architectures is a Data Lake. If you are curious about the implementation and services for a Data Lake with AWS, have a look at those blogposts. An architecture which provides transparency about the Data in the Data Lake and makes it smoothly available for further analytics in a Data Warehouse, is called a Data Lakehouse. Click here.
